Multilevel IRT Modeling in Practice with the Packagemlirt
نویسندگان
چکیده
منابع مشابه
Multilevel IRT Modeling in Practice with the Package mlirt
Variance component models are generally accepted for the analysis of hierarchical structured data. A shortcoming is that outcome variables are still treated as measured without an error. Unreliable variables produce biases in the estimates of the other model parameters. The variability of the relationships across groups and the group-effects on individuals’ outcomes differ substantially when ta...
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A structural multilevel model is presented where some of the variables cannot be observed directly but are measured using tests or questionnaires. Observed dichotomous or ordinal polytomous response data serve to measure the latent variables using an item response theory model. The latent variables can be defined at any level of the multilevel model. A Bayesian procedure Markov chain Monte Carl...
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Previous work has shown that multilevel modeling can be a valuable technique for epidemiologic analysis. The complexity of using this approach, however, continues to restrict its general application. A critical factor is the lack of flexible and appropriate software for multilevel modeling. SAS provides a macro, GLIMMIX, that can be used for multilevel modeling, but that is not sufficient for a...
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چکیده ندارد.
Multilevel Regression and Multilevel Structural Equation Modeling
Multilevel modeling in general concerns models for relationships between variables defined at different levels of a hierarchical data set, which is often viewed as a multistage sample from a hierarchically structured population. Common applications are individuals within groups, repeated measures within individuals, longitudinal modeling, and cluster randomized trials. This chapter treats the m...
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ژورنال
عنوان ژورنال: Journal of Statistical Software
سال: 2007
ISSN: 1548-7660
DOI: 10.18637/jss.v020.i05